Check computer science papers: citations, claims and retractions

Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing

Du, M., Wang, K., Xia, Z., & Zhang, Y. (2020) · IEEE Transactions on Big Data

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 111

This Thing Called Fairness: Disciplinary Confusion Realizing a Value in Technology

Mulligan, D. K., Kroll, J. A., Kohli, N., & Wong, R. Y. (2019) · Proceedings of the ACM on Human-Computer Interaction

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 111

Extracting biological age from biomedical data via deep learning: too much of a good thing?

Pyrkov, T. V., Slipensky, K., Barg, M., Kondrashin, A., Zhurov, B., Zenin, A., Pyatnitskiy, M., Menshikov, L., Markov, S., & Fedichev, P. O. (2018) · Scientific Reports

Machine Learning in HealthcarePhysical Activity and Health

Cited by 111

FReeNet: Multi-Identity Face Reenactment

Zhang, J., Zeng, X., Wang, M., Pan, Y., Liu, L., Liu, Y., Ding, Y., & Fan, C. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face recognition and analysisGenerative Adversarial Networks and Image Synthesis

Cited by 111

How does HCI Understand Human Agency and Autonomy?

Bennett, D., Metatla, O., Roudaut, A., & Mekler, E. D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionCybernetics and Technology in Society

Cited by 111

MIMIC-IV-Note: Deidentified free-text clinical notes

Johnson, A., Pollard, T., Horng, S., Celi, L. A., & Mark, R. (2023) · PhysioNet

Machine Learning in HealthcareBiomedical Text Mining and Ontologies

Cited by 111

Face–Iris Multimodal Biometric Identification System

Ammour, B., Boubchir, L., Bouden, T., & Ramdani, M. (2020) · Electronics

Biometric Identification and SecurityFace and Expression Recognition

Cited by 111

Learning from Games: HCI Design Innovations in Entertainment Software

Dyck, J., Pinelle, D., Brown, B., & Gutwin, C. (2003) · Canadian Human-Computer Communications Society and A K Peters Ltd.

Computer Graphics and Visualization TechniquesDigital Games and Media

Cited by 111

Meta-Learning-Based Incremental Few-Shot Object Detection

Cheng, M., Wang, H., & Long, Y. (2022) · IEEE Transactions on Circuits and Systems for Video Technology

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 111

LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models

Almodovar, C., Sabrina, F., Karimi, S., & Azad, S. (2024) · IEEE Transactions on Network and Service Management

Software System Performance and ReliabilityNetwork Security and Intrusion Detection

Cited by 111

Integrating probabilistic models of perception and interactive neural networks: a historical and tutorial review

McClelland, J. L. (2013) · Frontiers in Psychology

Bayesian Modeling and Causal InferenceReading and Literacy Development

Cited by 111

Advances in global change research require open science by individual researchers

Wolkovich, E. M., Regetz, J., & O'Connor, M. I. (2012) · Global Change Biology

Research Data Management PracticesData Analysis and Archiving

Cited by 111

Flexible zero trust architecture for the cybersecurity of industrial IoT infrastructures

Zanasi, C., Russo, S., & Colajanni, M. (2024) · Ad Hoc Networks

Software-Defined Networks and 5GNetwork Security and Intrusion Detection

Cited by 111

SECNLP: A survey of embeddings in clinical natural language processing

Kalyan, K. S., & Sangeetha, S. (2020) · Journal of Biomedical Informatics

Machine Learning in HealthcareTopic Modeling

Cited by 111

A Systematic Review of the Effects of Automatic Scoring and Automatic Feedback in Educational Settings

Hahn, M. G., Navarro, S. M. B., De La Fuente Valentin, L., & Burgos, D. (2021) · IEEE Access

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 111

Program synthesis using conflict-driven learning

Feng, Y., Martins, R., Bastani, O., & Dillig, I. (2018) · Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation

Evolutionary Algorithms and ApplicationsLogic, programming, and type systems

Cited by 111

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

Brazil, G., Kumar, A., Straub, J., Ravi, N., Johnson, J., & Gkioxari, G. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 111

Unsupervised Evaluation of Interactive Dialog with DialoGPT

Mehri, S., & Eskenazi, M. (2020) · Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

Speech and dialogue systemsAI in Service Interactions

Cited by 111

Reinforcement Learning with Perturbed Rewards

Wang, J., Liu, Y., & Li, B. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Reinforcement Learning in RoboticsAdversarial Robustness in Machine Learning

Cited by 111

The quantum adiabatic optimization algorithm and local minima

Reichardt, B. W. (2004) · Proceedings of the thirty-sixth annual ACM symposium on Theory of computing

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 110

Don’t Just Tell Me, Ask Me: AI Systems that Intelligently Frame Explanations as Questions Improve Human Logical Discernment Accuracy over Causal AI explanations

Danry, V., Pataranutaporn, P., Mao, Y., & Maes, P. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 110

The benefits and dangers of anthropomorphic conversational agents

Peter, S., Riemer, K., & West, J. D. (2025) · Proceedings of the National Academy of Sciences

Ethics and Social Impacts of AISocial Robot Interaction and HRI

Cited by 110

Machine Unlearning: Linear Filtration for Logit-based Classifiers

Baumhauer, T., Schöttle, P., & Zeppelzauer, M. (2020) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 110

A two-step convolutional neural network based computer-aided detection scheme for automatically segmenting adipose tissue volume depicting on CT images

Wang, Y., Qiu, Y., Thai, T., Moore, K., Liu, H., & Zheng, B. (2017) · Computer Methods and Programs in Biomedicine

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 110